Investigating the correlation between dusty days and climatic variables and water intake of Hamon lagoon using multivariate regression modeling

Dust storms are one of the natural phenomena that have affected many arid and semi-arid regions of the world in recent decades And it has increased significantly in the past years And as a result, it has had many harmful effects on the residents of the areas, so that the living conditions are very difficult in many areas due to the large amount of dust. Unfortunately, due to climate changes, including the decrease in rainfall, which on the other hand leads to the barrenness of the land surface and soil erosion, the conditions for the transport of fine dust can be provided, and when there are storms, a lot of fine dust is carried towards residential areas, and this affects It has a negative effect on the economy, health and environment. Fine dust enters the atmosphere affected by various factors including atmospheric conditions, characteristics of the earth's surface and characteristics (temperature, rain, wind, soil). Desert and devoid of vegetation are among the natural resources. Therefore, the most important factors affecting the intensity of dust are climatic changes and land surface conditions, so that with the decrease of rainfall and the decrease of water resources, especially in wetlands and the increase of barren lands, conditions prone to dust increase. Sistan plain is one of the important regions of the country which is very negatively affected by storms. Because, the lack of drainage of the Hamon wetland has led to the desertification of many areas of the wetland, and this has caused many of the wetlands to be transported to residential areas during dust storms, causing the destruction of agricultural and residential lands, damage to infrastructure, and many heart diseases. and be respiratory. Therefore, it is very important to investigate the factors affecting the intensity of dust in Sistan region so that necessary measures can be taken to manage and plan dust control. Therefore, the purpose of this research is to investigate the frequency of days with dust in relation to climatic variables (temperature, rainfall and wind speed) and water intake of Hamon lagoon.

In this research, the degree of correlation between the frequency of dusty days in relation to climatic variables and water intake of Hamon wetland in the Sistan plain in the period (2011-2021) was investigated. For this purpose, the average annual data of temperature, rainfall, wind speed and catchment area of Hamon lagoon were used in the studied time period. The data used were obtained from the meteorological station of Zabul. In order to use the data of the catchment area of Hamon lagoon, satellite images related to Landsat 7 and Landsat 8 satellites for 11 years (2011-2021) have been used.in order to prepare a map for extracting NDVI of water resources from Landsat satellite images related to the years 2011 to 2021 were used. The water resources index was used in the studied years. Then, the water layer was extracted by reclassification from the spectral index of each year and prepared as a Boolean layer of zero and one. Pearson's correlation coefficient was used in order to investigate the correlation between dusty days with climatic variables and the catchment area of Hamon lagoon. For modeling, multivariable regression was used, multivariable regression shows the change rate of one variable for other variables, and in other words, the rate of change in the dependent variable that occurs due to a unit change in the independent variable. In this method, a multi-equation A variable is used that summarizes the relationship between the dependent variable and the independent variables in a formula using the measured values. In this model, the number of days with dust is selected as the dependent variable, and the variables, temperature, rainfall, wind speed and water intake of Hamon lagoon are selected as independent variables. The coefficients of the equation for each variable are calculated and determined based on its importance in predicting the criterion variables. The degree of correlation between predictor variables is shown by coefficients.

The purpose of this research was to investigate the intensity of correlation and model the relationship between the frequency of days with dust storms and the water intake variables of Hamon lagoon, wind speed, rainfall and temperature in Zabul station. The results showed that the highest correlation coefficient with the number of days with dust is related to the wind speed with a value of 0.808, which shows the highest correlation. The correlation coefficient of temperature with the number of days with dust shows a significant and positive relationship of 0.422. The correlation coefficient of rainfall with the number of days with dust shows a significant and inverse relationship of -0.333, in addition to this, the frequency of dusty days with Hamon lagoon drainage has an inverse correlation coefficient with a value of -0.748. Because with the lack of dewatering of the Hamon wetland and the drying of the wetland bed and the reduction of vegetation, as a result of wind erosion, the wetland bed becomes the main centers of dust. And with the wind blowing, if the wind speed is high, a significant amount of sediments are transported from the wetland bed to the residential areas. Multivariate regression modeling between dust and the studied parameters showed that Hamon lagoon drainage and wind speed have a great effect on dust. According to the correlation coefficient of the studied variables, it was found that the water intake status of Hamon wetland has a greater effect on dusty days in the region than rainfall and temperature, so that with the increase of 1000 hectares, the water intake of Hamon wetland will decrease by about 0.3 units of dusty days. became. The results of multivariate regression modeling for dusty days and studied parameters showed that Hamon lagoon water intake and wind speed have a great effect on the frequency of dusty days. According to the value of R2, 61% of the dependent variable (number of dusty days) is explained by the independent variables (wetland drainage status, wind speed, temperature and rainfall) entered into the model.